Cerebras Systems started with a decision few hardware teams were willing to attempt: build a processor the size of a dinner plate. Andrew Feldman, Co-Founder and CEO, saw that AI workloads kept running into limits traditional chips couldn’t solve. A wafer-scale processor keeps data on-silicon, cuts movement and power use, and gives models far greater memory bandwidth—capabilities conventional designs hadn’t achieved.
Feldman describes the current moment as an inference boom. He says Cerebras Systems delivers the fastest inference in the industry—by more than an order of magnitude—and supports workloads ranging from coding assistants and deep scientific research to agentic systems and sovereign AI infrastructure. Adoption spans individual engineers using the self-service cloud to nations deploying large-scale clusters.
Throughout the conversation, Feldman explains how that original engineering decision enabled capabilities conventional architectures struggled to reach and why those capabilities align with the AI workloads scaling the fastest today.